Assignment help

While this weeks topic highlighted the uncertainty of Big Data, the author identified the following as areas for future research.  Pick one of the following for your Research paper:

• Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.
• The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.
• New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.
• More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.
• Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.

Your paper should meet these requirements: 

• Be approximately four to six pages in length, not including the required cover page and reference page.
• Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.
• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing

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Fall 2020 – Data Science & Big Data Analy (ITS-836-A01) – First Bi-Term • Week 3 Research Paper Big Data and Business Intelligence

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Big_Data_Analytics_and_Business_Intelligence x

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Running head: DATA 1

DATA 7

Big Data Analytics integration with Business Intelligence

Bharat Chekuri

University of the Cumberlands

ITS-836 Data Science & Big Data Analysis

Lo’ai Tawalbeh

09/10/2020

Introduction

Big data is a great volume of data that is gathered through external and internal sources. Having huge information in an organization requires diverse, like tools,

techniques, and architecture. Big data has both unstructured and structured that is used business daily. The amount of data does not matter but what the organiza-
tion uses the data for is what truly matters. Huge data can be evaluated for the insight that offers leads to proper decisions along with strategic business techniques.
In this paper, we will discuss a Fortune 1000 organization, along with how it is connected to big data. This paper will analyze Walmart Stores’ organization as the for-
tune 1000 company. About Wal-Mart

Walmart Stores is a well-known multinational retail corporation that is located in the United States. The organization is an organization that operates chain hypermar-
kets, discounted department stores, and grocery stores. Walmart headquarters is founded in Arkansas, Bentonville. The company was started by Sam Walton in

the year 1962. Walmart’s company is an organization that is an innovative thinking organization. The company is totally focused on saving people money, which makes
the company more affordable and attractive to clients (Erevelles et al., 2016). Approach to Big Data and Business Intelligence

The company gets over 240 million clients coming to the 10 900 stores and diverse active websites across the globe This makes the Company one of the biggest retail

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The company gets over 240 million clients coming to the 10,900 stores and diverse, active websites across the globe. This makes the Company one of the biggest retail
shops across the globe. The company approach to business Intelligence and Big Data is the fact that it has a big data ecosystem where is procedures on diverse

Terabytes of new petabytes and data every day. This evaluation is viewed to protect millions of items and diverse clients from diverse sources. The basic aim of the or-
ganization is utilizing huge data analytics by enhancing the shopping experiencing for clients, browsing websites, Walmart, and other regions that are significant to the
organization. It is seen that huge data solutions at the organization that is improved with the view of redesigning the international website. With this strategy to big
data, the organization has changed decision making in the company sector that has changed in the repeated and advance in the online outcome. The big data evalua-
tion has also been in a position to notify the value of transformation the company created by evaluation of sales after and before bid data analytics were utilized to
change the retail commerce approach giant for the e-commerce business. The company is viewed to utilize Hadoop information in the transformation of a huge data
strategy. The company works on the savings charter that is being utilized by the company to ensure that their company spends little to avoid too many expenses on
purchasing products. This is the application that signals clients whenever competition minimizes product cost the clients purchase. The e-receipts applications

are utilized to offer clients with the electronic copies of products they are willing to buy. There exists a mapping application for Walmart Company that utilizes
Hadoop, where it handles the most advanced maps of the stores located internationally. This location maps assists in explaining the exact location of these chain
stores.

What is Wal-Mart doing, right? Walmart, as the company, is working so hard to increase the sales number that it sells to its clients. There are different approaches

that the company is using to increase these sales. The company works on saving customer savings; that is why they focus on making more sales with little profit and
full of customer’s satisfaction. For example, the company is working on launching new products in its stores so that it might meet all the customer’s needs (Oussous et
al., 2018). The company is introducing new products by making use of the social media platforms to introduce the products the online retailers whereby they under-
stand what product is trending and what customers are looking for. This information form social media platforms are relevant in ensuring that the company advances
the number of product sales that is mostly needed by the public. When Walmart identifies the products needed by the public, they focus on ensuring that they bring
the products to the public at that point of need (Sanders, 2016). Walmart is working through the development of proper predictive analytics. The company uses big
data analytics that is behind the success of the company. The organization has advanced the shipping policy for the items founded on big data analytics, which makes
the company meet the needs of their clients (Schweikart, 2000). Walmart is doing okay by making good use of predictive analytics and advance the number of sales eli-
gible for the free shipping costs. The introduction of analytics improves customers’

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experience. Lastly, the company is doing right since it is making sure that the organization produces products that are customized according to client recommenda-
tions. Clients love when their ideas are relevant and receive products that are according to their own specifications. The company is seen those huge data algorithms
at the company assist in the evaluation of the purchase of credit cards. This offers specialized customers recommendation founded on the purchase history of

the company. What is Wal-Mart doing wrong? There exist not issues that are seen amid Walmart and its strategy to big data. However, what is need is the fact that the
company should be in a position to determine effective strategies that increase the scope of integration of huge data and company operations. This will enhance that
the organization stays overhead of the company competition and bring new customers on board. How to Improve

The idea that the company stores can operate on advancing their view or strategy of huge data analytics is by the utilization of market-based analysis. This is a rele-
vant approach in the classification pf serving clients better and shopping trips hence advancing the client’s experience (Singh et al., 2017). Conclusion

Walmart’s company is an organization that is an innovative thinking organization. The company is totally focused on saving people money, which makes the company
more affordable and attractive to clients. The big data evaluation has also been in a position to notify the value of transformation the company created by evalu-

ation of sales after and before bid data analytics were utilized to change the retail commerce approach giant for the e-commerce business. Walmart is doing okay by
making good use of predictive analytics and advance the number of sales eligible for the free shipping costs. The introduction of analytics improves customers’ experi-
ence..

References

Erevelles, S., Fukawa, N., & Swayne, L

. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-

904. doi:10.1016/j.jbusres.2015.07.001

Oussous, A., Benjelloun, F., Ait Lahcen, A., & Belfkih, S. (2018). Big data technologies: A survey. Journal of King Saud University – Computer and Information

Sciences, 30(4), 431-448. doi:10.1016/j.jksuci.2017.06.001

Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48. doi:10.1525/cmr.2016.58.3.26

Schweikart, L. (2000). Walton, Samuel Moore (1918-1992), founder of Wal-Mart stores. American National Biography Online. doi:10.1093/anb/9780198606697.ar-

ticle.1002202

Singh, M., Ghutla, B., Lilo Jnr, R., Mohammed, A. F., & Rashid, M. A. (2017). Walmart’s sales data analysis – A big data analytics perspective. 2017 4th Asia-Pacific

World Congress on Computer Science and Engineering (APWC on CSE). doi:10.1109/apwconcse.2017.00028

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Student paper 98%

Student paper 100%

Student paper 100%

vicsecretgarden 63%

Student paper 85%

Student paper 100%

Student paper 87%

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Big Data Analytics integration with Busi-
ness Intelligence

Original source

Integration of Big Data Analytics with
Business Intelligence

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University of the Cumberlands

Original source

University of Cumberlands

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Student paper

ITS-836 Data Science & Big Data Analysis

Original source

ITS 836 – Data Science & Big Data
Analysis

4
Student paper

09/10/2020

Original source

09.04.2020

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Student paper

Big data is a great volume of data that is
gathered through external and internal
sources.

Original source

Big data is known as the huge volume of
data that is gathered through external
and internal sources

5
Student paper

About Wal-Mart

Original source
About Wal-Mart
5
Student paper

The company was started by Sam Walton
in the year 1962.

Original source

The company was based in the year 1962
by Walton Sam

5
Student paper

Approach to Big Data and Business
Intelligence

Original source
Approach to Big Data and Business
Intelligence
5
Student paper

The company approach to business Intel-
ligence and Big Data is the fact that it has
a big data ecosystem where is proce-
dures on diverse Terabytes of new
petabytes and data every day. This evalu-
ation is viewed to protect millions of
items and diverse clients from diverse
sources. The basic aim of the organiza-
tion is utilizing huge data analytics by en-
hancing the shopping experiencing for
clients, browsing websites, Walmart, and
other regions that are significant to the
organization. It is seen that huge data so-
lutions at the organization that is im-
proved with the view of redesigning the
international website.

Original source

The companies approach business Intelli-
gence, and Big Data is the fact that it has
a huge data ecosystem where it works on
processing diverse Terabytes of the new
petabytes and new data every day (Singh,
et al., 2017) This evaluation is viewed to
cover diverse products, as well as the di-
verse clients, from diverse sources The
basic objective of the organization utiliz-
ing big data analytics is to allow the shop-
ping experience for clients, Wal-Mart
website browsing, as well as other ideas
that are significant for the organization It
is viewed that big data solutions at the
organization are improved with the idea
of redesigning the global website

9/14/2020 Originality Report

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Student paper 63%

Student paper 67%

Student paper 100%

Student paper 90%

Student paper 81%

Student paper 64%

Student paper 100%
5
Student paper

With this strategy to big data, the organi-
zation has changed decision making in
the company sector that has changed in
the repeated and advance in the online
outcome. The big data evaluation has
also been in a position to notify the value
of transformation the company created
by evaluation of sales after and before
bid data analytics were utilized to change
the retail commerce approach giant for
the e-commerce business.

Original source

With the big data approach, the organiza-
tion has changes decision making in the
enterprise sector, which has caused the
increase and repeated in online sales Big
data analysts can recognize the value of
transformation the company made by
evaluating the sales after and before bid
information analytics were utilized to
change the retail huge commerce ap-
proach (Erevelles et al., 2016)

5
Student paper

The e-receipts applications are utilized to
offer clients with the electronic copies of
products they are willing to buy.

Original source

The eReceipt apps are utilized to offer
clients with electronic copies of issues
they buy

5
Student paper

What is Wal-Mart doing, right?

Original source

What is Wal-Mart doing, right

5
Student paper

This offers specialized customers recom-
mendation founded on the purchase his-
tory of the company. What is Wal-Mart
doing wrong?

Original source

This offers specialized recommendation
to clients founded on the purchase histo-
ry What is Wal-Mart doing wrong

5
Student paper

How to Improve The idea that the com-
pany stores can operate on advancing
their view or strategy of huge data ana-
lytics is by the utilization of market-based
analysis. This is a relevant approach in
the classification pf serving clients better
and shopping trips hence advancing the
client’s experience (Singh et al., 2017).

Original source

How to Improve The idea that Wal-Mart
organization stores can operate on ad-
vancing their approach to big data analyt-
ics is by the utilization of market basket
evaluation This is relevant in the classifi-
cation shopping trips as well as serving
clients better, hence advancing the
client’s experience

5
Student paper

The big data evaluation has also been in
a position to notify the value of transfor-
mation the company created by evalua-
tion of sales after and before bid data an-
alytics were utilized to change the retail
commerce approach giant for the e-com-
merce business.

Original source

Big data analysts can recognize the value
of transformation the company made by
evaluating the sales after and before bid
information analytics were utilized to
change the retail huge commerce ap-
proach (Erevelles et al., 2016)

5
Student paper

Erevelles, S., Fukawa, N., & Swayne, L.

Original source
Erevelles, S., Fukawa, N., & Swayne, L

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Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
5
Student paper

Big data consumer analytics and the
transformation of marketing. Journal of
Business Research, 69(2), 897-904.
doi:10.1016/j.jbusres.2015.07.001 Ous-
sous, A., Benjelloun, F., Ait Lahcen, A., &
Belfkih, S.

Original source

Big data consumer analytics and the
transformation of marketing Journal of
Business Research, 69(2), 897-904
doi:10.1016/j.jbusres.2015.07.001 Ous-
sous, A., Benjelloun, F., Ait Lahcen, A., &
Belfkih, S

5
Student paper

Big data technologies:

Original source

Big data technologies

5
Student paper

Journal of King Saud University – Comput-
er and Information Sciences, 30(4), 431-
448. doi:10.1016/j.jksuci.2017.06.001

Original source

Journal of King Saud University – Comput-
er and Information Sciences, 30(4), 431-
448 doi:10.1016/j.jksuci.2017.06.001

5
Student paper

How to use big data to drive your supply
chain. California Management Review,
58(3), 26-48.
doi:10.1525/cmr.2016.58.3.26

Original source

How to use big data to drive your supply
chain California Management Review,
58(3), 26-48
doi:10.1525/cmr.2016.58.3.26

5
Student paper

Walton, Samuel Moore (1918-1992),
founder of Wal-Mart stores. American
National Biography Online.
doi:10.1093/anb/9780198606697.article.1
002202 Singh, M., Ghutla, B., Lilo Jnr, R.,
Mohammed, A.

Original source

Walton, Samuel Moore (1918-1992),
founder of Wal-Mart stores American Na-
tional Biography Online
doi:10.1093/anb/9780198606697.article.1
002202 Singh, M., Ghutla, B., Lilo Jnr, R.,
Mohammed, A

5
Student paper

F., & Rashid, M.

Original source

F., & Rashid, M

5
Student paper

Walmart’s sales data analysis – A big data
analytics perspective. 2017 4th Asia-Pa-
cific World Congress on Computer Sci-
ence and Engineering (APWC on CSE).
doi:10.1109/apwconcse.2017.00028

Original source

Walmart’s sales data analysis – A big data
analytics perspective 2017 4th Asia-Pacif-
ic World Congress on Computer Science
and Engineering (APWC on CSE)
doi:10.1109/apwconcse.2017.00028

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BigDataAnalyticsandtheInternetofThings.…

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Running head:

BIG DATA ANALYTICS 1

BIG DATA ANALYTICS 1

Big Data Analytics and the Internet of Things

Bharat Chekuri

University of the Cumberland’s

ITS-836 Data Science & Big Data Analysis

Lo’ai Tawalbeh

09/06/2020

Big Data Analytics and the Internet of Things In recent years, the use of communication and information technology has significantly impacted the evolution of com-
puter-aided manufacturing. Data analytics has proven efficient in extracting crucial business elements. With the evolution in the manufacturing industry, smart

manufacturing is common, and the Internet of Things plays a crucial role in connecting the physical aspects of manufacturing to the digital space of decision-making
algorithms and computing platforms. This sector comprises of diverse manufacturing controllers and equipment that are interdependent with other computing plat-
forms through wireless and wired communication links. There is an influx of data traffic generated from the IoT that is either unstructured, semi-structured, and struc-

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tured that is generated in real-time. There are many benefits associated with the IoT, such as reducing machine downtime, improving product quality, and im-

proving factory production and operation. However, it is also associated with challenges in the data analytics life cycle. Benefits of Big Data Analytics for Manufacturing
IoT The process of manufacturing involves a large amount of data that engulfs raw material supply, product distribution, logistics, and manufacturing. It is necessary
to analyze big data regularly such that critical information can be extracted. The benefits of big data analytics in Manufacturing IoT includes; Improving Factory

Production and Operations Data derived from customer demands and manufacturing is important in improving machinery use, consequently improving factory oper-
ations (Dey et al., 2018). For instance, some products are affected by seasonal or weather conditions like winter clothing during rainy seasons. Forecasting such condi-
tions would prioritize machinery assets and the raw materials needed to meet the resultant demand. Reduces Machine Downtime Different data reflecting machinery
status can be collected by deploying consistent sensors in the product line, identifying possible failures before they occur (Bi & Cochran, 2014). Automatic assembly
lines also balance loads from multiple pieces of machinery through sensory data that determines excess machine loads. Improving Product Quality. Analyzing

market demand and consumer needs is important in improving the product’s design, positively impacting its demand and similar improvements (Côrte-Real et al.,
2020). Analyzing manufacturing data is also important during product manufacturing, as it reduces the number of defective products by identifying the underlying
problem. Consequentially, the product’s quality is improved. Enhance Supply Chain Efficiency Supply risk management in the manufacturing IoT is made possible
through the proliferation of tags, RFID, and other sensors during supplier, manufacturing, and transportation (Côrte-Real et al., 2020). This factor leads to a large
amount of supply chain data that, in turn, predict risks, plans logistics, and predict delivery times, among others. Analyzing inventory data also reduces holding costs
and promotes safety stock levels (Dey et al., 2018). Additionally, big data analytics on the IoT aids in planning and scheduling of accurate logistic plans, ultimately lead-
ing to system efficiency.

Improving Customer Experience Different companies collect information from different sources, such as social media platforms, retailers, distributors, and sales part-
ners. Big data analytics, in this regard, offers solutions that are prescriptive, predictive, and descriptive, enabling companies to improve their product delivery,

quality, and design, as well as after-sales support (Dai et al., 2019). This positively impacts the customer experience. For instance, in the food industry, IoT data analyt-
ics guarantee food safety. Challenges of Big Data Analytics for IoT When determining the challenges of big data analytics concerning manufacturing IoT, the following
characteristics are considered; first, large social and business value, data is generated in real-time, data exists in heterogeneous forms, and data has vast volumes (Bi
& Cochran, 2014). These unique features can be problematic leading to the following challenges; Data Acquisition Data acquisition involves the issues involved in data
transmission and data collection. Some of these challenges include; Data Representation Difficulties IoT occurs in different dimensions, heterogeneous struc-

tures, and types. Since manufacturing data can be categorized into unstructured, semi-structured, and structured data, it poses a challenge for how exactly it should
be represented under big data analytics (Ahmed et al., 2017). Efficiency in Data Transmission The question of how large amounts of data should be transmitted and
stored becomes a challenge because; first, energy inefficiencies cause major constraints in wireless industrial systems like wireless sensor networks (Dey et al., 2018).

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Additionally, wireless communication systems sometimes get overwhelmed with increased bandwidth consumption. Difficulties in Data Storage and Processing Data
Integration Data generated from manufacturing IoT exists in different heterogeneous features and types, hence making it crucial to integrate these data types to im-
plement efficient data analytics schemes (Dai et al., 2019). However, given the diverse nature of the types of manufacturing IoT data, it becomes challenging to inte-
grate them. Data Redundancy There is a likelihood of data redundancy that occurs as a result of spatial and temporal redundancy from the raw data derived from IoT.
This factor affects subsequent data analysis and poses a challenge as to how best to overcome redundancy. Data Compression and Data Cleaning The large amounts
of data and errors recorded from the sensors make the process of data cleaning difficult. It is, therefore, crucial to implement effective measures that detect and clean
errors while compressing manufacturing IoT (Dai et al., 2019). Additionally, it is not easy to design and create effective data storage mechanisms, given the nature of
manufacturing IoT data. Data storage is critical in value extraction and data analysis. Persistency and Reliability of Data Storage It is critical to ensure the persistence
and reliability of manufacturing IoT data storage systems. The challenge, however, comes in the form of meeting the big data analytics requirements while considering
costs incurred due to the vast amount of data (Côrte-Real et al., 2020). Scalability Other than the problems incurred in storage, there comes a challenge of scalable
storage systems or big data analytics. There is a likelihood of in-feasibility of the conventional databases as a result of the vast amounts of data sets of manufacturing
IoT (Dai et al., 2019). Consequentially, there is a need to introduce new storage paradigms that would support tremendously large data storage systems for big

data analytics. Spatial and Temporal Correlation Since manufacturing IoT data is temporally and spatially correlated, it becomes challenging to extract and manage
useful information from spatially or temporally correlated IoT data (Dai et al., 2019). Efficient Data Mining Schemes Given the increased volumes of manufacturing IoT,
there is a challenge that arises given the uncertainty of erroneous manufacturing IoT data features and data errors likely to occur. Also, applying conventional multi-
pass data-mining schemes would not be feasible as a result of the large data volumes. Privacy and Security. Data security and privacy become challenging to

maintain despite numerous conventional privacy-preserving data analytical schemes (Ahmed et al., 2017). These schemes may not apply to the manufacturing IoT giv-
en the volume of data that needs to be analyzed. Conclusion Overall, collecting data and monitoring all activities is critical in manufacturing IoT. The data collected is
important in improving daily operations and create useful and efficient impacts on productivity. The benefits often outweigh the challenges, but the latter is often as-
sociated with data validity, transformation rate, solution costs, and data integration. Processing large amounts of data can be problematic, given its diverse nature and
consistent heterogeneous features.

References

Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in the Internet of

Things. Computer Networks, 129, 459-471. Retrieved from https://doi.org/10.1016/j.comnet.2017.06.013

Bi, Z., & Cochran, D

. (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), 249-265. Retrieved from

https://doi.org/10.1080/23270012.2014.992985

Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging the Internet of things and big data analytics initiatives in European and American firms: Is data

quality a way to extract business value?. Information & Management, 57(1), 103141. Retrieved from https://doi.org/10.1016/j.im.2019.01.003

Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2019). Big data analytics for manufacturing Internet of things: opportunities, challenges, and enabling tech-

nologies. Enterprise Information Systems, 1-25. Retrieved from https://doi.org/10.1080/17517575.2019.1633689

Dey, N., Hassanien, A. E., Bhatt, C., Ashour, A., & Satapathy, S. C. (Eds.). (2018). Internet of things and big data analytics toward next-generation intelligence

(pp. 3-549). Berlin: Springer. Retrieved from https://doi.org/10.1080/17517575.2019.163368

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Source Matches (32)

Student paper 89%

jkoolcloud 86%

Student paper 100%

Student paper 81%

Student paper 63%

Student paper 63%
Student paper 63%

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1
Student paper

BIG DATA ANALYTICS 1 BIG DATA ANA-
LYTICS 1

Original source

Big Data Science Analytics 1 Big Data Sci-
ence Analytics 1

2
Student paper

Big Data Analytics and the Internet of
Things

Original source

Big Data Analytics, healthcare, Internet of
Things

3
Student paper

University of the Cumberland’s

Original source
University of the Cumberland’s
4
Student paper

ITS-836 Data Science & Big Data Analysis

Original source

ITS 836 Data Science and Big Data
Analytics

5
Student paper

09/06/2020

Original source

06/28/2020

1
Student paper

With the evolution in the manufacturing
industry, smart manufacturing is com-
mon, and the Internet of Things plays a
crucial role in connecting the physical as-
pects of manufacturing to the digital
space of decision-making algorithms and
computing platforms.

Original source

During this evolution, Internet of Things
plays an important role in connecting the
physical environment of manufacturing
to the cyberspace of computing plat-
forms and decision-making algorithms,
consequently forming a Cyber-Physical
System

6
Student paper

There are many benefits associated with
the IoT, such as reducing machine down-
time, improving product quality, and im-
proving factory production and
operation.

Original source

reducing machine downtime, improving
product quality and design, and improv-
ing factory operations and productions

7
Student paper

The benefits of big data analytics in Man-
ufacturing IoT includes;

Original source

Benefits of Big Data Analytics for Manu-
facturing IoT

1
Student paper

Improving Product Quality.

Original source

Improving product quality

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Student paper 75%

Student paper 67%

Student paper 74%

Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%

ProQuest document 78%

Student paper 100%
8
Student paper

Big data analytics, in this regard, offers
solutions that are prescriptive, predictive,
and descriptive, enabling companies to
improve their product delivery, quality,
and design, as well as after-sales support
(Dai et al., 2019).

Original source

Customer satisfaction can be improved
by big data analytics on customer data,
which offers descriptive, predictive, and
prescriptive solutions to enable compa-
nies to improve product design, quality,
delivery, warrant, and after-sales support
(Dai et al., 2019)

1
Student paper

Data Representation Difficulties IoT oc-
curs in different dimensions, heteroge-
neous structures, and types. Since manu-
facturing data can be categorized into
unstructured, semi-structured, and struc-
tured data, it poses a challenge for how
exactly it should be represented under
big data analytics (Ahmed et al., 2017).

Original source

Manufacturing of IoT data has different
types, heterogeneous structures, and
various dimensions For example, manu-
facturing data can be categorized into
structured data, semi-structured and un-
structured data

9
Student paper

Consequentially, there is a need to intro-
duce new storage paradigms that would
support tremendously large data storage
systems for big data analytics.

Original source

As a result, new storage paradigms need
to be proposed to support large scale
data storage systems for big data
analytics

10
Student paper

Privacy and Security.

Original source

Privacy and security

3
Student paper

Ahmed, E., Yaqoob, I., Hashem, I.

Original source

Ahmed, E., Yaqoob, I., Hashem, I

3
Student paper

T., Khan, I., Ahmed, A.

Original source

T., Khan, I., Ahmed, A

3
Student paper

A., Imran, M., & Vasilakos, A.

Original source

A., Imran, M., & Vasilakos, A

3
Student paper

The role of big data analytics in the Inter-
net of Things. Computer Networks, 129,
459-471.

Original source

The role of big data analytics in Internet
of Things Computer Networks, 129, 459-
471

11
Student paper

Retrieved from
https://doi.org/10.1016/j.comnet.2017.06
.013

Original source

Retrieved from https://doi.org/10.1016/j

12
Student paper

Bi, Z., & Cochran, D.

Original source
Bi, Z., & Cochran, D

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Student paper 100%

Student paper 69%

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hotpaper 100%

Student paper 100%

Student paper 93%

Student paper 100%
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12
Student paper

Big data analytics with applications. Jour-
nal of Management Analytics, 1(4), 249-
265.

Original source

Big data analytics with applications Jour-
nal of Management Analytics, 1(4), 249-
265

13
Student paper

Retrieved from
https://doi.org/10.1080/23270012.2014.9
92985

Original source

Retrieved from
https://doi.org/10.1080/17517575.2019.1
633689

7
Student paper

Côrte-Real, N., Ruivo, P., & Oliveira, T.

Original source

Côrte-Real, N., Ruivo, P., & Oliveira, T

7
Student paper

Leveraging the Internet of things and big
data analytics initiatives in European and
American firms:

Original source

Leveraging internet of things and big
data analytics initiatives in European and
American firms

14
Student paper

Is data quality a way to extract business
value?.

Original source

Is data quality a way to extract business
value

3
Student paper

Information & Management, 57(1),
103141.

Original source

Information & Management, 57(1),
103141

7
Student paper

Retrieved from

https://doi.org/10.1016/j.im.2019.01.003

Original source
https://doi.org/10.1016/j.im.2019.01.003
15
Student paper

N., Wang, H., Xu, G., Wan, J., & Imran, M.

Original source

N., Wang, H., Xu, G., Wan, J., & Imran, M

15
Student paper

Big data analytics for manufacturing In-
ternet of things: opportunities, chal-
lenges, and enabling technologies. Enter-
prise Information Systems, 1-25.

Original source

Big data analytics for manufacturing in-
ternet of things opportunities, challenges
and enabling technologies Enterprise In-
formation Systems, 1-25

13
Student paper

Retrieved from
https://doi.org/10.1080/17517575.2019.1
633689

Original source
Retrieved from
https://doi.org/10.1080/17517575.2019.1
633689

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Student paper 100%
Student paper 100%

Student paper 100%12

Student paper

Dey, N., Hassanien, A. E., Bhatt, C.,
Ashour, A., & Satapathy, S.

Original source

Dey, N., Hassanien, A E., Bhatt, C.,
Ashour, A., & Satapathy, S

12
Student paper

Internet of things and big data analytics
toward next-generation intelligence (pp.

Original source

Internet of things and big data analytics
toward next-generation intelligence (pp

13
Student paper
Retrieved from
https://doi.org/10.1080/17517575.2019.1
633689
Original source
Retrieved from
https://doi.org/10.1080/17517575.2019.1
633689